Ailin Asadinejad

Examination of incentive based demand response in western connection reduced model

In this paper, an incentive base demand response (DR) program is proposed that has advantages for smaller residential consumers. In this model, a Load Serving Entity (LSE) needs to determine desired load reduction and an adequate incentive payment for their customers through the DR program. The program should be simple to implement for both the customer and LSE while achieving savings. Two different thresholds above market variable price aredered as the trigger for the proposed DR program: constant and a variable optimum threshold that is found by the optimization proposed in this paper. In this optimization framework, the savings for the LSE and customer is considered as well as the client convenience based on the number of requests for load change in each day and season under the DR program. Results show a constant threshold has more impact on locational marginal price (LMP) average and volatility in the high load season but a variable threshold can achieve benefits throughout the year.

Residential customers elasticity estimation and clustering based on their contribution at incentive based demand response

Incentive-based demand response (IBDR) is an important category of demand response (DR) programs with large untapped potential, especially in the residential sector. Understanding customers elasticity is key to effective design of incentives. However due to limited information, price-based elasticity is needed in IBDR modeling as well. In this work, customer elasticity for an IBDR program is calculated using data from two national surveys and integrated with a detailed residential load model. There are three important aspects about elasticity estimation in this work. First, the elasticity is specific to the structure of the incentives. Second, an elasticity at the individual appliance level in residential sector is more effective for designing incentives than one for aggregate load of the feeder. Third, if elasticity can be classified based on customers contribution and incentive expectations, then targeted incentives can be developed. All of these factors are novel idea for elasticity estimation. Main motivation behind this study is to show necessity of accurate customer modeling for IBDR programs. Distinction between elasticity of each appliance as well as each customer group could lead to huge difference in results of IBDR programs.

Error Analysis of Customer Baseline Load (CBL) Calculation Methods for Residential Customers

Federal Energy Regulatory Commission (FERC) 745 order has created an environment that allows demand response owners to sell their load reduction in the wholesale market. One of the main challenges that independent system operators and utilities face is developing customer baseline load (CBL) calculation methods that work satisfactorily in this new environment. Consequently, it is critical that these methods need to be evaluated from the error performances perspective. In this paper, error analysis of CBL calculation methods for residential customers is carried out theoretically and empirically. To perform theoretical analysis, the utility function of customers is analyzed to determine the existence of the economic incentives for gaming and inefficient consumption as well as studying the impact of inaccuracy on the social welfare loss. Furthermore, to perform the empirical analysis, well-established CBL calculation methods, HighXofY (New York ISO, well known as NYISO), LowXofY, MidXofY, exponential moving average (New England ISO, well known as ISONE), and regression are first introduced and, then, utilized to calculate the CBL. A dataset consisting of 262 residential customers is used for this analysis. In addition, the error analysis is performed using accuracy and bias metrics. To reach a valid conclusion about the overall performance of CBL methods, an economic analysis of a hypothetical peak time rebate (PTR) program is carried out. According to the results of the case study, the utility pays at least half of its revenue as a rebate solely due to inaccuracy of CBL methods. In addition, it is demonstrated that PTR creates inefficiencies in the residential sector because of the failure of CBL calculation methods to accurately predict the customers’ load profile on the event day.

An investigation of the relationship between accuracy of customer baseline calculation and efficiency of Peak Time Rebate program

In this paper, the relationship between accuracy of Customer Baseline (CBL) calculation and efficiency of Peak Time Rebate (PTR) program for residential customers is investigated. To perform the analysis, well-established CBL calculation methods, HighXofY(NYISO), LowXofY, MidXofY, exponential moving average(ISONE) and regression are first introduced and then utilized to calculate the CBL. A dataset consisting of 262 residential customers is used for this analysis. In addition, the error analysis is performed using accuracy and bias metrics. Furthermore, to reach a valid conclusion about overall performance of CBL methods, an economic analysis of a PTR program is carried out. According to the results, in the case study, utility pays at least half of its revenue as a rebate merely due to the inaccuracy of CBL methods. In addition, it is shown that PTR causes a lot of inefficiencies in the residential sector because of the failure of CBL calculation methods to predict the customers load profile on event day.

Impact of Incentive Based Demand Response on large scale renewable integration

The large scale deployment of renewable generation is generally seen as the most promising option for displacing fossil fuel generators, especially coal-fired power plants. A key challenge in integrating Renewable Energy Resources (RERs) is to find approaches that ensure long term sustainability and economic profit. One approach for mitigating the variability issue of integrating RERs is Demand Response (DR). The majority of current research is focuses on the role of DR for reliability support while economic concerns of RERs are barely addressed. In this paper, we investigate how DR can provide a potential solution to improve economic integration of RERs. More specifically, we propose Incentive Based DR (IBDR) programs, which is generally more attractive than real-time pricing programs for small customers. The proposed optimization framework in this paper finds an adequate amount of load change and incentive payments at each hour using a behavior model of customers. For this case study, the retirement of seven coal-fired power plants and expansion of RERs from less than 5% to 30%, is simulated for one year using data in the reduced WECC 240-bus system. Results show although renewable expansion could lead to benefit loss for utilities and sharp changes in market price, the proposed IBDR program could minimize these impacts.

Economic analysis of wind and CAES hybrid system using biomass based energy storage

Intermittent output of renewable resources is the main obstacle in front of higher penetration of them. A hybrid system of sustainable resource and energy storage is an appropriate solution to compensate periodic power output of a stand-alone system. In this paper a system consists of wind turbine generator with compress air energy storage (CAES) is studied. The CAES system generally needs a combustion tank and natural gas as a fuel that could cause some problems like instability at fuel cost, environmental pollution and so on. In this study, using biogas instead of natural gas is proposed to decrease disadvantage of using fossil fuels. Rather than other complicate gasifier technology, the traditional method of digestion is proposed that has more economic and environmental justification to use especially in rural areas. The proposed method has high technical and economic potentials to implement in most central villages in Iran.

Distribution of load change in industrial demand: A DOE approach

Estimating distribution of load change in response to demand management programs has been targeted in many researches, mostly based on economical and business sale-price models. In this paper a bottom-up analysis approach will be considered. As the first step consumers decision making process has been approximated using mathematical modeling. In this step a novel optimization model for production scheduling will be introduced which takes into account different energy consumption states. In the second step a design of experiment (DOE) model will be developed base on different energy and production factors. In the third step, two new distributions will be introduced based on a variable selected in DOE analysis. The proposed distributions can be obtained based on significant variables and be utilized in market analysis and more specifically, in stochastic analysis.